- Fix an error on using data.frame objects as data (#112).

- Minor fix on the plotting function.

- Now, hBayesDM has both R and Python version, with same models included! You can run hBayesDM with a language you prefer!
- Models in hBayesDM are now specified as YAML files. Using the YAML files, R and Python codes are generated automatically. If you want to contribute hBayesDM by adding a model, what you have to do is just to write a Stan file and to specify its information! You can find how to do in the hBayesDM wiki (https://github.com/CCS-Lab/hBayesDM/wiki).
- Model functions try to use parameter estimates using variational Bayesian methods as its initial values for MCMC sampling by default (#96). If VB estimation fails, then it uses random values instead.
- The
`data`

argument for model functions can handle a data.frame object (#2, #98). `choiceRT_lba`

and`choiceRT_lba_single`

are temporarily removed since their codes are not suitable to the new package structure. We plan to re-add the models in future versions.- The Cumulative Model for Cambridge Gambling Task is added (
`cgt_cm`

; #108).

- The
`tau`

parameter in all models for the risk aversion task is modified to be bounded to [0, 30] (#77, #78). `bart_4par`

is fixed to compute subject-wise log-likelihood (#82).`extract_ic`

is fixed for its wrong`rep`

function usage (#94, #100).- The drift rate (
`delta`

parameter) in`choiceRT_ddm`

and`choiceRT_ddm_single`

is unbounded and now it is estimated between [-Inf, Inf] (#95, #107). - Fix a preprocessing error in
`choiceRT_ddm`

and`choiceRT_ddm_single`

(#95, #109). - Fix
`igt_orl`

for a wrong Matt trick operation (#110).

- Add three new models for the bandit4arm task:
`bandit4arm_2par_lapse`

,`bandit4arm_lapse_decay`

and`bandit4arm_singleA_lapse`

. - Fix various (minor) errors.

- Make it usable without manually loading
`rstan`

. - Remove an annoying warning about using
`..insensitive_data_columns`

.

- Now, in default, you should build a Stan file into a binary for the first time to use it. To build all the models on installation, you should set an environmental variable
`BUILD_ALL`

to`true`

before installation. - Now all the implemented models are refactored using
`hBayesDM_model`

function. You don’t have to change anything to use them, but developers can easily implement new models now! - We added a Kalman filter model for 4-armed bandit task (
`bandit4arm2_kalman_filter`

; Daw et al., 2006) and a probability weighting function for general description-based tasks (`dbdm_prob_weight`

; Erev et al., 2010; Hertwig et al., 2004; Jessup et al., 2008). - Initial values of parameter estimation for some models are updated as plausible values, and the parameter boundaries of several models are fixed (see more on issue #63 and #64 in Github).
- Exponential and linear models for choice under risk and ambiguity task now have four model regressors:
`sv`

,`sv_fix`

,`sv_var`

, and`p_var`

. - Fix the Travix CI settings and related codes to be properly passed.

- Update the dependencies on rstan (>= 2.18.1)
- No changes on model files, as same as the version 0.6.2

- Fix an error on choiceRT_ddm (#44)

- Solve an issue with built binary files.
- Fix an error on peer_ocu with misplaced parentheses.

- Add new tasks (Balloon Analogue Risk Task, Choice under Risk and Ambiguity Task, Probabilistic Selection Task, Risky Decision Task (a.k.a. Happiness task), Wisconsin Card Sorting Task)
- Add a new model for the Iowa Gambling Task (igt_orl)
- Change priors (Half-Cauchy(0, 5) –> Half-Cauchy(0, 1) or Half-Normal(0, 0.2)
- printFit function now provides LOOIC weights and/or WAIC weights

- Add models for the Two Step task
- Add models without indecision point parameter (alpha) for the PRL task (prl_*_woa.stan)
- Model-based regressors for the PRL task are now available
- For the PRL task & prl_fictitious.stan & prl_fictitious_rp.stan –> change the range of alpha (indecision point) from [0, 1] to [-Inf, Inf]

- Support variational Bayesian methods (vb=TRUE)
- Allow posterior predictive checks, except for drift-diffusion models (inc_postpred=TRUE)
- Add the peer influence task (Chung et al., 2015, USE WITH CAUTION for now and PLEASE GIVE US FEEDBACK!)
- Add ‘prl_fictitious_rp’ model
- Made changes to be compatible with the newest Stan version (e.g., // instead of # for commenting).
- In ’prl_*’ models, ‘rewlos’ is replaced by ‘outcome’ so that column names and labels would be consistent across tasks as much as possible.
- Email feature is disabled as R mail package does not allow users to send anonymous emails anymore.
- When outputs are saved as a file (*.RData), the file name now contains the name of the data file.

- Add a choice reaction time task and evidence accumulation models
- Drift diffusion model (both hierarchical and single-subject)
- Linear Ballistic Accumulator (LBA) model (both hierarchical and single-subject)

- Add PRL models that can fit multiple blocks
- Add single-subject versions for the delay discounting task (
`dd_hyperbolic_single`

and`dd_cs_single`

). - Standardize variable names across all models (e.g.,
`rewlos`

–>`outcome`

for all models) - Separate versions for CRAN and GitHub. All models/features are identical but the GitHub version contains precompilled models.

- Remove dependence on the modeest package. Now use a built-in function to estimate the mode of a posterior distribution.
- Rewrite the “printFit” function.

- Made several changes following the guidelines for R packages providing interfaces to Stan.
- Stan models are precompiled and models will run immediately when called.
- The default number of chains is set to 4.
- The default value of
`adapt_delta`

is set to 0.95 to reduce the potential for divergences. - The “printFit” function uses LOOIC by default. Users can select WAIC or both (LOOIC & WAIC) if needed.

- Add help files
- Add a function for checking Rhat values (rhat).
- Change a link to its tutorial website

- Use wide normal distributions for unbounded parameters (gng_* models).
- Automatic removal of rows (trials) containing NAs.

- Add a function for plotting individual parameters (plotInd)

- Add a new task: the Ultimatum Game
- Add new models for the Probabilistic Reversal Learning and Risk Aversion tasks
- ‘bandit2arm’ -> change its name to ‘bandit2arm_delta’. Now all model names are in the same format (i.e., TASK_MODEL).
- Users can extract model-based regressors from gng_m* models
- Include the option of customizing control parameters (adapt_delta, max_treedepth, stepsize)
- ‘plotHDI’ function -> add ‘fontSize’ argument & change the color of histogram

- All models: Fix errors when indPars=“mode”
- ra_prospect model: Add description for column names of a data (*.txt) file

- Change standard deviations of ‘b’ and ‘pi’ priors in gng_* models

Initially released.